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Статті в журналах з теми "Computational Characterization"

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Автор: Grafiati

Опубліковано: 28 вересня 2022

Оновлено: 28 січня 2023

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1

Bienkowska, Jadwiga. "Computational characterization of proteins." Expert Review of Proteomics 2, no. 1 (January 2005): 129–38. http://dx.doi.org/10.1586/14789450.2.1.129.

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2

Zeng, Xiongzhi, Wei Hu, Xiao Zheng, Jin Zhao, Zhenyu Li, and Jinlong Yang. "Computational characterization of nanosystems." Chinese Journal of Chemical Physics 35, no. 1 (February 2022): 1–15. http://dx.doi.org/10.1063/1674-0068/cjcp2111233.

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Nanosystems play an important role in many applications. Due to their complexity, it is challenging to accurately characterize their structure and properties. An important means to reach such a goal is computational simulation, which is grounded on ab initio electronic structure calculations. Low scaling and accurate electronic-structure algorithms have been developed in recent years. Especially, the efficiency of hybrid density functional calculations for periodic systems has been significantly improved. With electronic structure information, simulation methods can be developed to directly obtain experimentally comparable data. For example, scanning tunneling microscopy images can be effectively simulated with advanced algorithms. When the system we are interested in is strongly coupled to environment, such as the Kondo effect, solving the hierarchical equations of motion turns out to be an effective way of computational characterization. Furthermore, the first principles simulation on the excited state dynamics rapidly emerges in recent years, and nonadiabatic molecular dynamics method plays an important role. For nanosystem involved chemical processes, such as graphene growth, multiscale simulation methods should be developed to characterize their atomic details. In this review, we review some recent progresses in methodology development for computational characterization of nanosystems. Advanced algorithms and software are essential for us to better understand of the nanoworld.

3

Larsson, Mats. "Computational characterization of drawbeads." Journal of Materials Processing Technology 209, no. 1 (January 2009): 376–86. http://dx.doi.org/10.1016/j.jmatprotec.2008.02.009.

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4

Khan, Ishita K., and Daisuke Kihara. "Computational characterization of moonlighting proteins." Biochemical Society Transactions 42, no. 6 (November 17, 2014): 1780–85. http://dx.doi.org/10.1042/bst20140214.

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Moonlighting proteins perform multiple independent cellular functions within one polypeptide chain. Moonlighting proteins switch functions depending on various factors including the cell-type in which they are expressed, cellular location, oligomerization status and the binding of different ligands at different sites. Although an increasing number of moonlighting proteins have been experimentally identified in recent years, the quantity of known moonlighting proteins is insufficient to elucidate their overall landscape. Moreover, most moonlighting proteins have been identified as a serendipitous discovery. Hence, characterization of moonlighting proteins using bioinformatics approaches can have a significant impact on the overall understanding of protein function. In this work, we provide a short review of existing computational approaches for illuminating the functional diversity of moonlighting proteins.

5

Politzer, Peter, Jane S. Murray, Jorge M. Seminario, Pat Lane, M. Edward Grice, and Monica C. Concha. "Computational characterization of energetic materials." Journal of Molecular Structure: THEOCHEM 573, no. 1-3 (October 2001): 1–10. http://dx.doi.org/10.1016/s0166-1280(01)00533-4.

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6

Gao, Haixiang, Chengfeng Ye, Crystal M. Piekarski, and Jean'ne M. Shreeve. "Computational Characterization of Energetic Salts." Journal of Physical Chemistry C 111, no. 28 (July 2007): 10718–31. http://dx.doi.org/10.1021/jp070702b.

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7

Abukhdeir, Nasser Mohieddin. "Computational characterization of ordered nanostructured surfaces." Materials Research Express 3, no. 8 (August 4, 2016): 082001. http://dx.doi.org/10.1088/2053-1591/3/8/082001.

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8

Wodo, Olga, Srikanta Tirthapura, Sumit Chaudhary, and Baskar Ganapathysubramanian. "Computational characterization of bulk heterojunction nanomorphology." Journal of Applied Physics 112, no. 6 (September 15, 2012): 064316. http://dx.doi.org/10.1063/1.4752864.

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9

Slanina, Zdenĕk, and Shigeru Nagase. "A computational characterization of N2@C60." Molecular Physics 104, no. 20-21 (October 20, 2006): 3167–71. http://dx.doi.org/10.1080/00268970601041131.

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10

Glossman-Mitnik, Daniel. "Computational molecular characterization of Coumarin-102." Journal of Molecular Structure: THEOCHEM 911, no. 1-3 (October 2009): 105–8. http://dx.doi.org/10.1016/j.theochem.2009.07.006.

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11

Rubinstein, Nimrod D., Itay Mayrose, Dan Halperin, Daniel Yekutieli, Jonathan M. Gershoni, and Tal Pupko. "Computational characterization of B-cell epitopes." Molecular Immunology 45, no. 12 (July 2008): 3477–89. http://dx.doi.org/10.1016/j.molimm.2007.10.016.

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12

Shang, Yuan, Qikai Li, Lingyi Meng, Dong Wang, and Zhigang Shuai. "Computational characterization of organic photovoltaic devices." Theoretical Chemistry Accounts 129, no. 3-5 (March 30, 2011): 291–301. http://dx.doi.org/10.1007/s00214-011-0924-x.

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13

Slanina, Zdeněk, Filip Uhlík, Shigeru Nagase, Takeshi Akasaka, Ludwik Adamowicz, and Xing Lu. "A computational characterization of CO@C60." Fullerenes, Nanotubes and Carbon Nanostructures 25, no. 11 (November 2, 2017): 624–29. http://dx.doi.org/10.1080/1536383x.2017.1357548.

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14

Fellous, Jean-Marc, and Christiane Linster. "Computational Models of Neuromodulation." Neural Computation 10, no. 4 (May 1, 1998): 771–805. http://dx.doi.org/10.1162/089976698300017476.

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Computational modeling of neural substrates provides an excellent theoretical framework for the understanding of the computational roles of neuromodulation. In this review, we illustrate, with a large number of modeling studies, the specific computations performed by neuromodulation in the context of various neural models of invertebrate and vertebrate preparations. We base our characterization of neuromodulations on their computational and functional roles rather than on anatomical or chemical criteria. We review the main framework in which neuromodulation has been studied theoretically (central pattern generation and oscillations, sensory processing, memory and information integration). Finally, we present a detailed mathematical overview of how neuromodulation has been implemented at the single cell and network levels in modeling studies. Overall, neuromodulation is found to increase and control computational complexity.

15

WIEDERMANN, JIŘÍ, and DANA PARDUBSKÁ. "WIRELESS MOBILE COMPUTING AND ITS LINKS TO DESCRIPTIVE COMPLEXITY." International Journal of Foundations of Computer Science 19, no. 04 (August 2008): 887–913. http://dx.doi.org/10.1142/s0129054108006029.

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The Wireless Parallel Turing Machine (WPTM) is a new computational model recently introduced and studied by the authors. Its design captures important features of wireless mobile computing. In this paper we survey some results related to the descriptive complexity aspects of the new model. In particular, we show a tight relationship about (a) wireless parallel computing, (b) alternating, and (c) synchronized alternating Turing machines. This relationship opens, e.g., the road to circuit complexity by offering an elegant WPTM characterization of bounded-fan-in uniform circuit families, such as NC and NCi. The structural properties of computational graphs of WPTM computations inspire definitions of new complexity measures capturing important aspects of wireless computations: energy consumption and the number of broadcasting channels used during computation. These measures do not seem to have direct counterparts in alternating computations. We mention results related to these new structural measures, e.g., a polynomial time–bounded complexity hierarchy based on channel complexity, lying between P and PSPACE which seems to be incomparable to the standard polynomial–time alternating hierarchy.

16

Gossett, Eric M., Ellen B. Scanley, Yanhui Liu, Yanglin Li, Ze Liu, Sungwoo Sohn, Jan Schroers, Christine Broadbridge, and Todd C. Schwendemann. "Computational Nanocharacterization for Combinatorially Developed Bulk Metallic Glass." International Journal of High Speed Electronics and Systems 24, no. 03n04 (September 2015): 1520012. http://dx.doi.org/10.1142/s0129156415200128.

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Bulk metallic glasses synthesized at specialized facilities at Yale using magnetron cosputtering are sent to Southern Connecticut State University for elemental characterization. Characterization is done using a Zeiss Sigma VP SEM coupled with an Oxford EDS. Characterization is automated using control software provided by Oxford. Collected data is processed and visualized using computational methods developed internally. Processed data is then organized into a database suitable for web retrieval. This technique allows for the rapid characterization of a combinatorial wafer to be carried out in ~11 hours for a single wafer containing ~600 unique compounds.

17

Fuladi, Shadi, Sarah McGuinness, and Fatemeh Khalili-Araghi. "Computational characterization of claudin-15 strand flexibility." Biophysical Journal 121, no. 3 (February 2022): 464a. http://dx.doi.org/10.1016/j.bpj.2021.11.437.

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18

Fernandez-Gamiz, Unai, Mustafa Demirci, Mustafa İlbaş, Ekaitz Zulueta, Jose Antonio Ramos, Jose Manuel Lopez Guede, and Erol Kurt. "Computational characterization of an axial rotor fan." Journal of Energy Systems 1, no. 4 (December 29, 2017): 129–37. http://dx.doi.org/10.30521/jes.346660.

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19

Paik, Seung Hoon, Tae Ho Yoon, Sang Joon Shin, and Seung Jo Kim. "Computational Material Characterization of Active Fiber Composite." Journal of Intelligent Material Systems and Structures 18, no. 1 (October 10, 2006): 19–28. http://dx.doi.org/10.1177/1045389x06064347.

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20

Huang, Jialong, Chi Wang, Lijie Chang, Ya Zhang, Zhebin Wang, Lin Yi, and Wei Jiang. "Computational characterization of electron-beam-sustained plasma." Physics of Plasmas 26, no. 6 (June 2019): 063502. http://dx.doi.org/10.1063/1.5091466.

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21

Oveisi, Ali Reza, Pouya Karimi, Hojat Samareh Delarami, Saba Daliran, Ahmad Khorramabadi-zad, Mostafa Khajeh, Esmael Sanchooli, and Mansour Ghaffari-Moghaddam. "New porphyrins: synthesis, characterization, and computational studies." Molecular Diversity 24, no. 2 (May 6, 2019): 335–44. http://dx.doi.org/10.1007/s11030-019-09955-2.

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22

Geltmacher, Andrew B., Peter Matic, and Richard K. Everett. "Integrated experimental–computational characterization of TIMETAL 21S." Materials Science and Engineering: A 272, no. 1 (November 1999): 99–113. http://dx.doi.org/10.1016/s0921-5093(99)00545-6.

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23

Gracon, A., S. Pernecky, M. Milletti, J. Park, Y. Yuan, and H. Kim. "Computational Characterization of a Series of Eicosanoids." Letters in Drug Design & Discovery 2, no. 4 (June 1, 2005): 322–28. http://dx.doi.org/10.2174/1570180054038431.

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24

Zhao, Songtao, Erjun Kan, and Zhenyu Li. "Electride: from computational characterization to theoretical design." Wiley Interdisciplinary Reviews: Computational Molecular Science 6, no. 4 (March 29, 2016): 430–40. http://dx.doi.org/10.1002/wcms.1258.

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25

Nieboer, Jared, Johnathan P. Mack, Hélène P. A. Mercier, and Michael Gerken. "Synthesis, Characterization, and Computational Study of MoSF4." Inorganic Chemistry 49, no. 13 (July 5, 2010): 6153–59. http://dx.doi.org/10.1021/ic100766d.

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26

Chakraborty, Chinmay. "Computational approach for chronic wound tissue characterization." Informatics in Medicine Unlocked 17 (2019): 100162. http://dx.doi.org/10.1016/j.imu.2019.100162.

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27

Terwijn, Sebastiaan A., and Domenico Zambella. "Computational randomness and lowness." Journal of Symbolic Logic 66, no. 3 (September 2001): 1199–205. http://dx.doi.org/10.2307/2695101.

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AbstractWe prove that there are uncountably many sets that are low for the class of Schnorr random reals. We give a purely recursion theoretic characterization of these sets and show that they all have Turing degree incomparable to 0′. This contrasts with a result of Kučera and Terwijn [5] on sets that are low for the class of Martin-Löf random reals.

28

Cabessa, Jérémie, and Hava T. Siegelmann. "The Computational Power of Interactive Recurrent Neural Networks." Neural Computation 24, no. 4 (April 2012): 996–1019. http://dx.doi.org/10.1162/neco_a_00263.

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In classical computation, rational- and real-weighted recurrent neural networks were shown to be respectively equivalent to and strictly more powerful than the standard Turing machine model. Here, we study the computational power of recurrent neural networks in a more biologically oriented computational framework, capturing the aspects of sequential interactivity and persistence of memory. In this context, we prove that so-called interactive rational- and real-weighted neural networks show the same computational powers as interactive Turing machines and interactive Turing machines with advice, respectively. A mathematical characterization of each of these computational powers is also provided. It follows from these results that interactive real-weighted neural networks can perform uncountably many more translations of information than interactive Turing machines, making them capable of super-Turing capabilities.

29

Manea, Florin. "On Turing Machines Deciding According to the Shortest Computations." Axioms 10, no. 4 (November 13, 2021): 304. http://dx.doi.org/10.3390/axioms10040304.

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In this paper we propose and analyse from the computational complexity point of view several new variants of nondeterministic Turing machines. In the first such variant, a machine accepts a given input word if and only if one of its shortest possible computations on that word is accepting; on the other hand, the machine rejects the input word when all the shortest computations performed by the machine on that word are rejecting. We are able to show that the class of languages decided in polynomial time by such machines is PNP[log]. When we consider machines that decide a word according to the decision taken by the lexicographically first shortest computation, we obtain a new characterization of PNP. A series of other ways of deciding a language with respect to the shortest computations of a Turing machine are also discussed.

30

De Santis, Valerio. "Special Issue: Advances in Computational Electromagnetics." Magnetochemistry 7, no. 6 (June 21, 2021): 89. http://dx.doi.org/10.3390/magnetochemistry7060089.

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Recent advances in computational electromagnetics (CEMs) have made the full characterization of complex magnetic materials possible, such as superconducting materials, composite or nanomaterials, rare-earth free permanent magnets, etc [...]

31

Papayannopoulos, Philippos, Nir Fresco, and Oron Shagrir. "On Two Different Kinds of Computational Indeterminacy." Monist 105, no. 2 (March 9, 2022): 229–46. http://dx.doi.org/10.1093/monist/onab033.

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Abstract It is often indeterminate what function a given computational system computes. This phenomenon has been referred to as “computational indeterminacy” or “multiplicity of computations.” In this paper, we argue that what has typically been considered and referred to as the (unique) challenge of computational indeterminacy in fact subsumes two distinct phenomena, which are typically bundled together and should be teased apart. One kind of indeterminacy concerns a functional (or formal) characterization of the system’s relevant behavior (briefly: how its physical states are grouped together and corresponded to abstract states). Another kind concerns the manner in which the abstract (or computational) states are interpreted (briefly: what function the system computes). We discuss the similarities and differences between the two kinds of computational indeterminacy, their implications for certain accounts of “computational individuation” in the literature, and their relevance to different levels of description within the computational system. We also examine the inter-relationships between our proposed accounts of the two kinds of indeterminacy and the main accounts of “computational implementation.”

32

Brooks, Benjamin D., Adam Closmore, Juechen Yang, Michael Holland, Tina Cairns, Gary H. Cohen, and Chris Bailey-Kellogg. "Characterizing Epitope Binding Regions of Entire Antibody Panels by Combining Experimental and Computational Analysis of Antibody: Antigen Binding Competition." Molecules 25, no. 16 (August 11, 2020): 3659. http://dx.doi.org/10.3390/molecules25163659.

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Vaccines and immunotherapies depend on the ability of antibodies to sensitively and specifically recognize particular antigens and specific epitopes on those antigens. As such, detailed characterization of antibody–antigen binding provides important information to guide development. Due to the time and expense required, high-resolution structural characterization techniques are typically used sparingly and late in a development process. Here, we show that antibody–antigen binding can be characterized early in a process for whole panels of antibodies by combining experimental and computational analyses of competition between monoclonal antibodies for binding to an antigen. Experimental “epitope binning” of monoclonal antibodies uses high-throughput surface plasmon resonance to reveal which antibodies compete, while a new complementary computational analysis that we call “dock binning” evaluates antibody–antigen docking models to identify why and where they might compete, in terms of possible binding sites on the antigen. Experimental and computational characterization of the identified antigenic hotspots then enables the refinement of the competitors and their associated epitope binding regions on the antigen. While not performed at atomic resolution, this approach allows for the group-level identification of functionally related monoclonal antibodies (i.e., communities) and identification of their general binding regions on the antigen. By leveraging extensive epitope characterization data that can be readily generated both experimentally and computationally, researchers can gain broad insights into the basis for antibody–antigen recognition in wide-ranging vaccine and immunotherapy discovery and development programs.

33

Woolley, Jack M., Raúl Losantos, Diego Sampedro, and Vasilios G. Stavros. "Computational and experimental characterization of novel ultraviolet filters." Physical Chemistry Chemical Physics 22, no. 43 (2020): 25390–95. http://dx.doi.org/10.1039/d0cp04940a.

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34

Fiore, Vincenzo G., Dimitri Ognibene, Bryon Adinoff, and Xiaosi Gu. "A Multilevel Computational Characterization of Endophenotypes in Addiction." eneuro 5, no. 4 (July 2018): ENEURO.0151–18.2018. http://dx.doi.org/10.1523/eneuro.0151-18.2018.

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35

Luoto, Suvi, Ismaïl Hermelo, Elisa M. Vuorinen, Paavo Hannus, Juha Kesseli, Matti Nykter, and Kirsi J. Granberg. "Computational Characterization of Suppressive Immune Microenvironments in Glioblastoma." Cancer Research 78, no. 19 (June 19, 2018): 5574–85. http://dx.doi.org/10.1158/0008-5472.can-17-3714.

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36

Wang, Jianyun, Quan Li, Chris J. Pickard, Changfeng Chen, and Yanming Ma. "Computational discovery and characterization of new B2O phases." Physical Chemistry Chemical Physics 21, no. 5 (2019): 2499–506. http://dx.doi.org/10.1039/c8cp07161f.

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Our theoretical investigations have unraveled peculiar bonding characters in the current identified superconducting phases for B₂O at high pressure, especially the evolution of chemical bonds and electronic states associated with the B₁₂ icosahedral unit in the orthorhombic phase and the pseudo-layered trigonal phase.

37

Xia, Jianlong, Matthew R. Golder, Michael E. Foster, Bryan M. Wong, and Ramesh Jasti. "Synthesis, Characterization, and Computational Studies of Cycloparaphenylene Dimers." Journal of the American Chemical Society 134, no. 48 (November 20, 2012): 19709–15. http://dx.doi.org/10.1021/ja307373r.

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38

Zhuang, Houlong L., Michelle D. Johannes, Michael N. Blonsky, and Richard G. Hennig. "Computational prediction and characterization of single-layer CrS2." Applied Physics Letters 104, no. 2 (January 13, 2014): 022116. http://dx.doi.org/10.1063/1.4861659.

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39

de Lima, Cícero R., Sandro L. Vatanabe, Andres Choi, Paulo Henrique Nakasone, Rogério Felipe Pires, and Emílio Carlos Nelli Silva. "A biomimetic piezoelectric pump: Computational and experimental characterization." Sensors and Actuators A: Physical 152, no. 1 (May 2009): 110–18. http://dx.doi.org/10.1016/j.sna.2009.02.038.

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40

Ruiz-Anchondo, Teresita, та Daniel Glossman-Mitnik. "Computational characterization of the β,β-carotene molecule". Journal of Molecular Structure: THEOCHEM 913, № 1-3 (листопад 2009): 215–20. http://dx.doi.org/10.1016/j.theochem.2009.07.043.

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41

Muthukkumar, M., T. Bhuvaneswari, G. Venkatesh, C. Kamal, P. Vennila, Stevan Armaković, Sanja J. Armaković, Y. Sheena Mary, and C. Yohannan Panicker. "Synthesis, characterization and computational studies of semicarbazide derivative." Journal of Molecular Liquids 272 (December 2018): 481–95. http://dx.doi.org/10.1016/j.molliq.2018.09.123.

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42

Afonin, Kirill A., Wojciech Kasprzak, Eckart Bindewald, Praneet S. Puppala, Alex R. Diehl, Kenneth T. Hall, Tae Jin Kim, et al. "Computational and experimental characterization of RNA cubic nanoscaffolds." Methods 67, no. 2 (May 2014): 256–65. http://dx.doi.org/10.1016/j.ymeth.2013.10.013.

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43

Nikravesh, M. "Soft computing-based computational intelligent for reservoir characterization." Expert Systems with Applications 26, no. 1 (January 2004): 19–38. http://dx.doi.org/10.1016/s0957-4174(03)00119-2.

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44

Peralta-Inga, Z., J. S. Murray, M. Edward Grice, S. Boyd, C. J. O'Connor, and P. Politzer. "Computational characterization of surfaces of model graphene systems." Journal of Molecular Structure: THEOCHEM 549, no. 1-2 (August 2001): 147–58. http://dx.doi.org/10.1016/s0166-1280(01)00491-2.

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45

Xu, Qisong, and Jianwen Jiang. "Computational Characterization of Ultrathin Polymer Membranes in Liquids." Macromolecules 51, no. 18 (September 10, 2018): 7169–77. http://dx.doi.org/10.1021/acs.macromol.8b01387.

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46

Kabir, Md Zahirul, Nur Aziean Binti Hamzah, Hamidah Ghani, Saharuddin B. Mohamad, Zazali Alias, and Saad Tayyab. "Biophysical and computational characterization of vandetanib–lysozyme interaction." Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 189 (January 2018): 485–94. http://dx.doi.org/10.1016/j.saa.2017.08.051.

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47

Fernandez, Jose-Jesus. "Computational methods for materials characterization by electron tomography." Current Opinion in Solid State and Materials Science 17, no. 3 (June 2013): 93–106. http://dx.doi.org/10.1016/j.cossms.2013.03.002.

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